An Image is Worth Multiple Words: Multi-attribute Inversion for Constrained Text-to-Image Synthesis

TL;DR

This paper introduces MATTE, a multi-attribute inversion method for diffusion models that disentangles attributes like color, style, layout, and object from a single reference image, improving constrained text-to-image synthesis.

Contribution

The paper provides an extensive analysis of attribute capture in diffusion models and proposes MATTE, a novel inversion algorithm that disentangles multiple attributes across different model dimensions.

Findings

MATTE effectively disentangles multiple attributes in diffusion models.

Attributes like color and style are captured in the same layers, while layout and color are captured across timesteps.

The method improves constrained image synthesis by leveraging disentangled attribute representations.

Abstract

We consider the problem of constraining diffusion model outputs with a user-supplied reference image. Our key objective is to extract multiple attributes (e.g., color, object, layout, style) from this single reference image, and then generate new samples with them. One line of existing work proposes to invert the reference images into a single textual conditioning vector, enabling generation of new samples with this learned token. These methods, however, do not learn multiple tokens that are necessary to condition model outputs on the multiple attributes noted above. Another line of techniques expand the inversion space to learn multiple embeddings but they do this only along the layer dimension (e.g., one per layer of the DDPM model) or the timestep dimension (one for a set of timesteps in the denoising process), leading to suboptimal attribute disentanglement. To address the aforementioned gaps, the first contribution of this paper is an extensive analysis to determine which attributes are captured in which dimension of the denoising process. As noted above, we consider both the time-step dimension (in reverse denoising) as well as the DDPM model layer dimension. We observe that often a subset of these attributes are captured in the same set of model layers and/or across same denoising timesteps. For instance, color and style are captured across same U-Net layers, whereas layout and color are captured across same timestep stages. Consequently, an inversion process that is designed only for the time-step dimension or the layer dimension is insufficient to disentangle all attributes. This leads to our second contribution where we design a new multi-attribute inversion algorithm, MATTE, with associated disentanglement-enhancing regularization losses, that operates across both dimensions and explicitly leads to four disentangled tokens (color, style, layout, and object).